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Visual Prompting
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Visual Prompting is the task of streamlining computer vision processes by harnessing the power of prompts, inspired by the breakthroughs of text prompting in NLP. This innovative approach involves using a few visual prompts to swiftly convert an unlabeled dataset into a deployed model, significantly reducing development time for both individual projects and enterprise solutions.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Stepwise Decomposition and Dual-stream Focus: A Novel Approach for Training-free Camouflaged Object Segmentation
RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought
Grid-LOGAT: Grid Based Local and Global Area Transcription for Video Question Answering
A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis
DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models
VP Lab: a PEFT-Enabled Visual Prompting Laboratory for Semantic Segmentation
Vision Graph Prompting via Semantic Low-Rank Decomposition
Token Coordinated Prompt Attention is Needed for Visual Prompting
Black-Box Visual Prompt Engineering for Mitigating Object Hallucination in Large Vision Language Models
Zoomer: Adaptive Image Focus Optimization for Black-box MLLM
RadSAM: Segmenting 3D radiological images with a 2D promptable model
Visual and textual prompts for enhancing emotion recognition in video
NVSMask3D: Hard Visual Prompting with Camera Pose Interpolation for 3D Open Vocabulary Instance Segmentation
Visual Prompting for One-shot Controllable Video Editing without Inversion
EarthGPT-X: Enabling MLLMs to Flexibly and Comprehensively Understand Multi-Source Remote Sensing Imagery
Prompt-Guided Attention Head Selection for Focus-Oriented Image Retrieval
Is Temporal Prompting All We Need For Limited Labeled Action Recognition?
Towards Online Multi-Modal Social Interaction Understanding
VP-NTK: Exploring the Benefits of Visual Prompting in Differentially Private Data Synthesis
3DAxisPrompt: Promoting the 3D Grounding and Reasoning in GPT-4o
KUDA: Keypoints to Unify Dynamics Learning and Visual Prompting for Open-Vocabulary Robotic Manipulation
Chameleon: Fast-slow Neuro-symbolic Lane Topology Extraction
Towards Ambiguity-Free Spatial Foundation Model: Rethinking and Decoupling Depth Ambiguity
Towards Universal Text-driven CT Image Segmentation
The Role of Background Information in Reducing Object Hallucination in Vision-Language Models: Insights from Cutoff API Prompting
From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Framework
Personalization Toolkit: Training Free Personalization of Large Vision Language Models
Articulate AnyMesh: Open-Vocabulary 3D Articulated Objects Modeling
LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation
IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting
MedFocusCLIP : Improving few shot classification in medical datasets using pixel wise attention
GPT4Scene: Understand 3D Scenes from Videos with Vision-Language Models
Visual Prompting with Iterative Refinement for Design Critique Generation
Selective Visual Prompting in Vision Mamba
Inst-IT: Boosting Multimodal Instance Understanding via Explicit Visual Prompt Instruction Tuning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.